The maritime industry has long relied on human experience, intuition, and judgment. With Artificial Intelligence (AI) now entering this complex world, professionals are weighing both the potential benefits and the risks. This article introduces the purpose of our latest thought leadership report commissioned by Marcura, the questions it seeks to answer, and why emotions, governance, and trust are central to AI adoption.
Successful technology adoption depends on optimism, engagement, and motivation. While AI is widely seen as a valuable tool in the maritime industry, it also brings uncertainty and apprehension.
Beyond The Hype explores how maritime professionals perceive AI. In shipping, where experience and judgment are highly valued, new tools that appear to take over decision-making can feel unsettling. Some hesitation is rooted in real concerns, such as data privacy, cybersecurity, and regulatory uncertainty. Other fears, like job loss or control erosion are more nuanced and can often be addressed through training, education, and the right system architecture.
The report examines how AI is perceived by maritime professionals today and the source of resistance. It distinguishes between concerns that are grounded in real issues, such as data privacy and cybersecurity, and those that are more nuanced or speculative, such as fears around job displacement. By exploring these perceptions, we aim to provide a clearer picture of the opportunities and challenges AI presents to the maritime sector.
The research answers the following questions and provides insights, recommendations, and practical steps to help the industry enhance AI engagement and adoption:
- What are the emotional blockers to AI adoption and why do they exist?
- What are the perceived risks of AI and where are the sources of resistance?
- How can these risks be framed as opportunities?
- How ready are maritime organisations to adopt AI?
- How can maritime professionals best embrace AI to ensure employee engagement and achieve a good ROI?
Defining AI
Different types of AI exist and the language used to describe them plays a crucial role in shaping perceptions. Below are some key terms commonly used when discussing AI.
Agentic AI – Intelligent systems that are made up of autonomous “agents” that can reason, plan, and take multi-step actions toward goals, without ongoing human instruction. Unlike generative chatbots, they can execute tasks, such as booking travel or approving invoices, using tool integrations and memory capabilities. Agentic AI goes beyond natural language processing and sees decisions made independently while interacting with external environments. This is often seen in robotics.
Embedded AI – AI models or logic integrated directly into hardware or devices (edge systems) so they can process data and decide in real time, without needing to send it to the cloud.
Vertical AI – AI solutions tailor-built for specific industries or domains, leveraging deep sector knowledge and specialised data to deliver high-precision outcomes.
For further insight into the areas highlighted in this article, download our thought leadership report, Beyond The Hype, created in partnership with Marcura.
